1. Field of the Invention
The present invention relates to an image communication method and apparatus for transmitting image information, and the like.
2. Related Background Art
Conventionally, various color facsimile apparatuses for communicating color images have been proposed. In a color image, since color data of each of the color components, red (R), green (G), and blue (B), has 256 gradation levels between 0 to 255, the data volume is very large, and the communication time is prolonged, as compared to those of a black-and-white image. For this reason, it is very difficult to put such an apparatus for directly transmitting multi-value data into practical application.
As a method of compressing the data volume of a color image, each of R, G, and B data is binarized from 256 gradation levels between 0 to 255 to 2 gradation levels of 0 and 1, and binary data is coded by a conventional coding method in a facsimile apparatus such as MMR, MR, or the like.
However, when a received color binary image is color-processed, and is printed out, colors vary depending on the type used in binarizing method of binarizing the original multi-value image, and an image in colors different from those of the color image which was to be transmitted is undesirably printed out the receiver side.
Colors of an image to be transmitted are different from those of an image printed out by a receiver like in a case wherein image data which is binarized by a Fatning type dithering method is transmitted to a receiver which is adjusted to reproduce appropriate colors of an image which is binarized by a Bayer type dithering method.
There is also proposed a method of presuming multi-value data from a binary pattern of a rectangular region in a binary image by utilizing a neural network.
The method utilizing a neural network can realize good multi-value restoration by learning.
However, multi-value restoration by a neural network depends on the binarizing method of the binary data used in learning. Therefore, since various binarizing methods exist, even when a neural network which learns an image binarized by an error diffusion method is used in restoration of an image binarized by a dithering method, good multi-value data cannot always be obtained.